Eggshell crack detection based on acoustic impulse response and supervised pattern recognition
2009
Lin, H.,Jiangsu Univ., Zhenjiang (China). School of Food and Biological Engineering | Zhao, J.W.,Jiangsu Univ., Zhenjiang (China). School of Food and Biological Engineering | Chen, Q.S.,Jiangsu Univ., Zhenjiang (China). School of Food and Biological Engineering | Cai, J.R.,Jiangsu Univ., Zhenjiang (China). School of Food and Biological Engineering | Zhou, P.,Jiangsu Univ., Zhenjiang (China). School of Food and Biological Engineering
A system based on acoustic resonance was developed for eggshell crack detection. It was achieved by the analysis of the measured frequency response of eggshell excited with a light mechanism. The response signal was processed by a recursive least squares adaptive filter. Thus, the signal-to-noise ratio of the acoustic impulse response was remarkably enhanced. Five features (variables) were exacted from the response frequency signals. To develop a robust discrimination model, three pattern recognition algorithms (i.e. K-nearest neighbours, artificial neural network, and support vector machine) were examined comparatively in this work. Some parameters of the model were optimised by cross-validation in the building model. The experimental results showed that the performance of the support vector machine model is the best in comparison with k-nearest neighbours and artificial neural network models. The optimal support vector machine model was obtained with the identification rates of 95.1% in the calibration set, and 97.1% in the prediction set, respectively. Based on the results, it was concluded that the acoustic resonance system combined with the supervised pattern recognition has a significant potential for the cracked eggs detection.
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